Evidence map›Paper›PMID 39995567›Full record

ReviewIBRO neuroscience reports2025

Advancing Alzheimer's Therapy: Computational strategies and treatment innovations.

Jibon Kumar Paul, Abbeha Malik, Mahir Azmal, Tooba Gulzar, Muhammad Talal Rahim Afghan, Omar Faruk Talukder, Samar Shahzadi, Ajit Ghosh

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in IBRO neuroscience reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jibon Kumar PaulDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.
Abbeha MalikDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Pakistan.
Mahir AzmalDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.
Tooba GulzarDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Pakistan.
Muhammad Talal Rahim AfghanDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Pakistan.
Omar Faruk TalukderDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.
Samar ShahzadiDepartment of Bioinformatics, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Pakistan.
Ajit GhoshDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment.

Indexed as

Alzheimer therapyArtificial intelligenceComputational techniquesDrug designMachine learningNeurodegenerative disease

Identifiers

PMID39995567
PMCPMC11849200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.